import json, yaml, warnings, torch
import platform

warnings.filterwarnings("ignore")
torch.manual_seed(233333)
import os, sys

now_dir = os.getcwd()
tmp = os.path.join(now_dir, "TEMP")
os.makedirs(tmp, exist_ok=True)
os.environ["TEMP"] = tmp
import site

site_packages_root = "%s/runtime/Lib/site-packages" % now_dir
for path in site.getsitepackages():
    if "site-packages" in path:
        site_packages_root = path
os.environ["OPENBLAS_NUM_THREADS"] = "4"
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
with open("%s/users.pth" % (site_packages_root), "w") as f:
    f.write(
        "%s\n%s/tools\n%s/tools/damo_asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
        % (now_dir, now_dir, now_dir, now_dir, now_dir)
    )
import traceback

sys.path.append(now_dir)
import gradio as gr
from subprocess import Popen
from config import (
    python_exec,
    infer_device,
    is_half,
    exp_root,
    webui_port_main,
    webui_port_infer_tts,
    webui_port_uvr5,
    webui_port_subfix,
)
from tools.i18n.i18n import I18nAuto

i18n = I18nAuto()
from multiprocessing import cpu_count

n_cpu = cpu_count()

# 判断是否有能用来训练和加速推理的N卡
ngpu = torch.cuda.device_count()
gpu_infos = []
mem = []
if_gpu_ok = False

if torch.cuda.is_available() or ngpu != 0:
    for i in range(ngpu):
        gpu_name = torch.cuda.get_device_name(i)
        if any(
            value in gpu_name.upper()
            for value in [
                "10",
                "16",
                "20",
                "30",
                "40",
                "A2",
                "A3",
                "A4",
                "P4",
                "A50",
                "500",
                "A60",
                "70",
                "80",
                "90",
                "M4",
                "T4",
                "TITAN",
                "L",
            ]
        ):
            # A10#A100#V100#A40#P40#M40#K80#A4500
            if_gpu_ok = True  # 至少有一张能用的N卡
            gpu_infos.append("%s\t%s" % (i, gpu_name))
            mem.append(
                int(
                    torch.cuda.get_device_properties(i).total_memory
                    / 1024
                    / 1024
                    / 1024
                    + 0.4
                )
            )
if if_gpu_ok and len(gpu_infos) > 0:
    gpu_info = "\n".join(gpu_infos)
    default_batch_size = min(mem) // 2
else:
    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
    default_batch_size = 1
gpus = "-".join([i[0] for i in gpu_infos])

pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
pretrained_gpt_name = (
    "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
)


def get_weights_names():
    SoVITS_names = [pretrained_sovits_name]
    for name in os.listdir(SoVITS_weight_root):
        if name.endswith(".pth"):
            SoVITS_names.append(name)
    GPT_names = [pretrained_gpt_name]
    for name in os.listdir(GPT_weight_root):
        if name.endswith(".ckpt"):
            GPT_names.append(name)
    return SoVITS_names, GPT_names


SoVITS_weight_root = "SoVITS_weights"
GPT_weight_root = "GPT_weights"
os.makedirs(SoVITS_weight_root, exist_ok=True)
os.makedirs(GPT_weight_root, exist_ok=True)
SoVITS_names, GPT_names = get_weights_names()


def change_choices():
    SoVITS_names, GPT_names = get_weights_names()
    return {"choices": sorted(SoVITS_names), "__type__": "update"}, {
        "choices": sorted(GPT_names),
        "__type__": "update",
    }


p_label = None
p_uvr5 = None
p_asr = None
p_tts_inference = None

system = platform.system()


def kill_process(pid):
    if system == "Windows":
        cmd = "taskkill /t /f /pid %s" % pid
    else:
        cmd = "kill -9 %s" % pid
    print(cmd)
    os.system(cmd)  ###linux上杀了webui，可能还会没杀干净。。。
    # os.kill(p_label.pid,19)#主进程#控制台进程#python子进程###不好使，连主进程的webui一起关了，辣鸡


def change_label(if_label, path_list):
    global p_label
    if if_label == True and p_label == None:
        cmd = '"%s" tools/subfix_webui.py --load_list "%s" --webui_port %s' % (
            python_exec,
            path_list,
            webui_port_subfix,
        )
        yield "打标工具WebUI已开启"
        print(cmd)
        p_label = Popen(cmd, shell=True)
    elif if_label == False and p_label != None:
        kill_process(p_label.pid)
        p_label = None
        yield "打标工具WebUI已关闭"


def change_uvr5(if_uvr5):
    global p_uvr5
    if if_uvr5 == True and p_uvr5 == None:
        cmd = '"%s" tools/uvr5/webui.py "%s" %s %s' % (
            python_exec,
            infer_device,
            is_half,
            webui_port_uvr5,
        )
        yield "UVR5已开启"
        print(cmd)
        p_uvr5 = Popen(cmd, shell=True)
    elif if_uvr5 == False and p_uvr5 != None:
        kill_process(p_uvr5.pid)
        p_uvr5 = None
        yield "UVR5已关闭"


def change_tts_inference(
    if_tts, bert_path, cnhubert_base_path, gpu_number, gpt_path, sovits_path
):
    global p_tts_inference
    if if_tts == True and p_tts_inference == None:
        os.environ["gpt_path"] = (
            gpt_path if "/" in gpt_path else "%s/%s" % (GPT_weight_root, gpt_path)
        )
        os.environ["sovits_path"] = (
            sovits_path
            if "/" in sovits_path
            else "%s/%s" % (SoVITS_weight_root, sovits_path)
        )
        os.environ["cnhubert_base_path"] = cnhubert_base_path
        os.environ["bert_path"] = bert_path
        os.environ["_CUDA_VISIBLE_DEVICES"] = gpu_number
        os.environ["is_half"] = str(is_half)
        os.environ["infer_ttswebui"] = str(webui_port_infer_tts)
        cmd = '"%s" GPT_SoVITS/inference_webui.py' % (python_exec)
        yield "TTS推理进程已开启"
        print(cmd)
        p_tts_inference = Popen(cmd, shell=True)
    elif if_tts == False and p_tts_inference != None:
        kill_process(p_tts_inference.pid)
        p_tts_inference = None
        yield "TTS推理进程已关闭"


def open_asr(asr_inp_dir):
    global p_asr
    if p_asr == None:
        cmd = '"%s" tools/damo_asr/cmd-asr.py "%s"' % (python_exec, asr_inp_dir)
        yield "ASR任务开启：%s" % cmd, {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }
        print(cmd)
        p_asr = Popen(cmd, shell=True)
        p_asr.wait()
        p_asr = None
        yield "ASR任务完成", {"__type__": "update", "visible": True}, {
            "__type__": "update",
            "visible": False,
        }
    else:
        yield "已有正在进行的ASR任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }


def close_asr():
    global p_asr
    if p_asr != None:
        kill_process(p_asr.pid)
        p_asr = None
    return (
        "已终止ASR进程",
        {"__type__": "update", "visible": True},
        {"__type__": "update", "visible": False},
    )


"""
            button1Ba_open.click(open1Ba, [batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D], [info1Bb,button1Ba_open,button1Ba_close])
            button1Ba_close.click(close1Ba, [], [info1Bb,button1Ba_open,button1Ba_close])
"""
p_train_SoVITS = None


def open1Ba(
    batch_size,
    total_epoch,
    exp_name,
    text_low_lr_rate,
    if_save_latest,
    if_save_every_weights,
    save_every_epoch,
    gpu_numbers1Ba,
    pretrained_s2G,
    pretrained_s2D,
):
    global p_train_SoVITS
    if p_train_SoVITS == None:
        with open("GPT_SoVITS/configs/s2.json") as f:
            data = f.read()
            data = json.loads(data)
        s2_dir = "%s/%s" % (exp_root, exp_name)
        os.makedirs("%s/logs_s2" % (s2_dir), exist_ok=True)
        data["train"]["batch_size"] = batch_size
        data["train"]["epochs"] = total_epoch
        data["train"]["text_low_lr_rate"] = text_low_lr_rate
        data["train"]["pretrained_s2G"] = pretrained_s2G
        data["train"]["pretrained_s2D"] = pretrained_s2D
        data["train"]["if_save_latest"] = if_save_latest
        data["train"]["if_save_every_weights"] = if_save_every_weights
        data["train"]["save_every_epoch"] = save_every_epoch
        data["train"]["gpu_numbers"] = gpu_numbers1Ba
        data["data"]["exp_dir"] = data["s2_ckpt_dir"] = s2_dir
        data["save_weight_dir"] = SoVITS_weight_root
        data["name"] = exp_name
        tmp_config_path = "TEMP/tmp_s2.json"
        with open(tmp_config_path, "w") as f:
            f.write(json.dumps(data))

        cmd = '"%s" GPT_SoVITS/s2_train.py --config "%s"' % (
            python_exec,
            tmp_config_path,
        )
        yield "SoVITS训练开始：%s" % cmd, {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }
        print(cmd)
        p_train_SoVITS = Popen(cmd, shell=True)
        p_train_SoVITS.wait()
        p_train_SoVITS = None
        yield "SoVITS训练完成", {"__type__": "update", "visible": True}, {
            "__type__": "update",
            "visible": False,
        }
    else:
        yield "已有正在进行的SoVITS训练任务，需先终止才能开启下一次任务", {
            "__type__": "update",
            "visible": False,
        }, {"__type__": "update", "visible": True}


def close1Ba():
    global p_train_SoVITS
    if p_train_SoVITS != None:
        kill_process(p_train_SoVITS.pid)
        p_train_SoVITS = None
    return (
        "已终止SoVITS训练",
        {"__type__": "update", "visible": True},
        {"__type__": "update", "visible": False},
    )


p_train_GPT = None


def open1Bb(
    batch_size,
    total_epoch,
    exp_name,
    if_save_latest,
    if_save_every_weights,
    save_every_epoch,
    gpu_numbers,
    pretrained_s1,
):
    global p_train_GPT
    if p_train_GPT == None:
        with open("GPT_SoVITS/configs/s1longer.yaml") as f:
            data = f.read()
            data = yaml.load(data, Loader=yaml.FullLoader)
        s1_dir = "%s/%s" % (exp_root, exp_name)
        os.makedirs("%s/logs_s1" % (s1_dir), exist_ok=True)
        data["train"]["batch_size"] = batch_size
        data["train"]["epochs"] = total_epoch
        data["pretrained_s1"] = pretrained_s1
        data["train"]["save_every_n_epoch"] = save_every_epoch
        data["train"]["if_save_every_weights"] = if_save_every_weights
        data["train"]["if_save_latest"] = if_save_latest
        data["train"]["half_weights_save_dir"] = GPT_weight_root
        data["train"]["exp_name"] = exp_name
        data["train_semantic_path"] = "%s/6-name2semantic.tsv" % s1_dir
        data["train_phoneme_path"] = "%s/2-name2text.txt" % s1_dir
        data["output_dir"] = "%s/logs_s1" % s1_dir

        os.environ["_CUDA_VISIBLE_DEVICES"] = gpu_numbers.replace("-", ",")
        os.environ["hz"] = "25hz"
        tmp_config_path = "TEMP/tmp_s1.yaml"
        with open(tmp_config_path, "w") as f:
            f.write(yaml.dump(data, default_flow_style=False))
        # cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" --train_semantic_path "%s/6-name2semantic.tsv" --train_phoneme_path "%s/2-name2text.txt" --output_dir "%s/logs_s1"'%(python_exec,tmp_config_path,s1_dir,s1_dir,s1_dir)
        cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" ' % (
            python_exec,
            tmp_config_path,
        )
        yield "GPT训练开始：%s" % cmd, {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }
        print(cmd)
        p_train_GPT = Popen(cmd, shell=True)
        p_train_GPT.wait()
        p_train_GPT = None
        yield "GPT训练完成", {"__type__": "update", "visible": True}, {
            "__type__": "update",
            "visible": False,
        }
    else:
        yield "已有正在进行的GPT训练任务，需先终止才能开启下一次任务", {
            "__type__": "update",
            "visible": False,
        }, {"__type__": "update", "visible": True}


def close1Bb():
    global p_train_GPT
    if p_train_GPT != None:
        kill_process(p_train_GPT.pid)
        p_train_GPT = None
    return (
        "已终止GPT训练",
        {"__type__": "update", "visible": True},
        {"__type__": "update", "visible": False},
    )


ps_slice = []


def open_slice(
    inp,
    opt_root,
    threshold,
    min_length,
    min_interval,
    hop_size,
    max_sil_kept,
    _max,
    alpha,
    n_parts,
):
    global ps_slice
    if os.path.exists(inp) == False:
        yield "输入路径不存在", {"__type__": "update", "visible": True}, {
            "__type__": "update",
            "visible": False,
        }
        return
    if os.path.isfile(inp):
        n_parts = 1
    elif os.path.isdir(inp):
        pass
    else:
        yield "输入路径存在但既不是文件也不是文件夹", {"__type__": "update", "visible": True}, {
            "__type__": "update",
            "visible": False,
        }
        return
    if ps_slice == []:
        for i_part in range(n_parts):
            cmd = (
                '"%s" tools/slice_audio.py "%s" "%s" %s %s %s %s %s %s %s %s %s'
                ""
                % (
                    python_exec,
                    inp,
                    opt_root,
                    threshold,
                    min_length,
                    min_interval,
                    hop_size,
                    max_sil_kept,
                    _max,
                    alpha,
                    i_part,
                    n_parts,
                )
            )
            print(cmd)
            p = Popen(cmd, shell=True)
            ps_slice.append(p)
        yield "切割执行中", {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }
        for p in ps_slice:
            p.wait()
        ps_slice = []
        yield "切割结束", {"__type__": "update", "visible": True}, {
            "__type__": "update",
            "visible": False,
        }
    else:
        yield "已有正在进行的切割任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }


def close_slice():
    global ps_slice
    if ps_slice != []:
        for p_slice in ps_slice:
            try:
                kill_process(p_slice.pid)
            except:
                traceback.print_exc()
        ps_slice = []
    return (
        "已终止所有切割进程",
        {"__type__": "update", "visible": True},
        {"__type__": "update", "visible": False},
    )


"""
inp_text=                           os.environ.get("inp_text")
inp_wav_dir=                        os.environ.get("inp_wav_dir")
exp_name=                           os.environ.get("exp_name")
i_part=                             os.environ.get("i_part")
all_parts=                          os.environ.get("all_parts")
os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
opt_dir=                            os.environ.get("opt_dir")#"/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
bert_pretrained_dir=                os.environ.get("bert_pretrained_dir")#"/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large"
"""
ps1a = []


def open1a(inp_text, inp_wav_dir, exp_name, gpu_numbers, bert_pretrained_dir):
    global ps1a
    if ps1a == []:
        config = {
            "inp_text": inp_text,
            "inp_wav_dir": inp_wav_dir,
            "exp_name": exp_name,
            "opt_dir": "%s/%s" % (exp_root, exp_name),
            "bert_pretrained_dir": bert_pretrained_dir,
        }
        gpu_names = gpu_numbers.split("-")
        all_parts = len(gpu_names)
        for i_part in range(all_parts):
            config.update(
                {
                    "i_part": str(i_part),
                    "all_parts": str(all_parts),
                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                    "is_half": str(is_half),
                }
            )
            os.environ.update(config)
            cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py' % python_exec
            print(cmd)
            p = Popen(cmd, shell=True)
            ps1a.append(p)
        yield "文本进程执行中", {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }
        for p in ps1a:
            p.wait()
        ps1a = []
        yield "文本进程结束", {"__type__": "update", "visible": True}, {
            "__type__": "update",
            "visible": False,
        }
    else:
        yield "已有正在进行的文本任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }


def close1a():
    global ps1a
    if ps1a != []:
        for p1a in ps1a:
            try:
                kill_process(p1a.pid)
            except:
                traceback.print_exc()
        ps1a = []
    return (
        "已终止所有1a进程",
        {"__type__": "update", "visible": True},
        {"__type__": "update", "visible": False},
    )


"""
inp_text=                           os.environ.get("inp_text")
inp_wav_dir=                        os.environ.get("inp_wav_dir")
exp_name=                           os.environ.get("exp_name")
i_part=                             os.environ.get("i_part")
all_parts=                          os.environ.get("all_parts")
os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
opt_dir=                            os.environ.get("opt_dir")
cnhubert.cnhubert_base_path=                os.environ.get("cnhubert_base_dir")
"""
ps1b = []


def open1b(inp_text, inp_wav_dir, exp_name, gpu_numbers, ssl_pretrained_dir):
    global ps1b
    if ps1b == []:
        config = {
            "inp_text": inp_text,
            "inp_wav_dir": inp_wav_dir,
            "exp_name": exp_name,
            "opt_dir": "%s/%s" % (exp_root, exp_name),
            "cnhubert_base_dir": ssl_pretrained_dir,
            "is_half": str(is_half),
        }
        gpu_names = gpu_numbers.split("-")
        all_parts = len(gpu_names)
        for i_part in range(all_parts):
            config.update(
                {
                    "i_part": str(i_part),
                    "all_parts": str(all_parts),
                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                }
            )
            os.environ.update(config)
            cmd = (
                '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py' % python_exec
            )
            print(cmd)
            p = Popen(cmd, shell=True)
            ps1b.append(p)
        yield "SSL提取进程执行中", {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }
        for p in ps1b:
            p.wait()
        ps1b = []
        yield "SSL提取进程结束", {"__type__": "update", "visible": True}, {
            "__type__": "update",
            "visible": False,
        }
    else:
        yield "已有正在进行的SSL提取任务，需先终止才能开启下一次任务", {
            "__type__": "update",
            "visible": False,
        }, {"__type__": "update", "visible": True}


def close1b():
    global ps1b
    if ps1b != []:
        for p1b in ps1b:
            try:
                kill_process(p1b.pid)
            except:
                traceback.print_exc()
        ps1b = []
    return (
        "已终止所有1b进程",
        {"__type__": "update", "visible": True},
        {"__type__": "update", "visible": False},
    )


"""
inp_text=                           os.environ.get("inp_text")
exp_name=                           os.environ.get("exp_name")
i_part=                             os.environ.get("i_part")
all_parts=                          os.environ.get("all_parts")
os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
opt_dir=                            os.environ.get("opt_dir")
pretrained_s2G=                     os.environ.get("pretrained_s2G")
"""
ps1c = []


def open1c(inp_text, exp_name, gpu_numbers, pretrained_s2G_path):
    global ps1c
    if ps1c == []:
        config = {
            "inp_text": inp_text,
            "exp_name": exp_name,
            "opt_dir": "%s/%s" % (exp_root, exp_name),
            "pretrained_s2G": pretrained_s2G_path,
            "s2config_path": "GPT_SoVITS/configs/s2.json",
            "is_half": str(is_half),
        }
        gpu_names = gpu_numbers.split("-")
        all_parts = len(gpu_names)
        for i_part in range(all_parts):
            config.update(
                {
                    "i_part": str(i_part),
                    "all_parts": str(all_parts),
                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                }
            )
            os.environ.update(config)
            cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py' % python_exec
            print(cmd)
            p = Popen(cmd, shell=True)
            ps1c.append(p)
        yield "语义token提取进程执行中", {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }
        for p in ps1c:
            p.wait()
        ps1c = []
        yield "语义token提取进程结束", {"__type__": "update", "visible": True}, {
            "__type__": "update",
            "visible": False,
        }
    else:
        yield "已有正在进行的语义token提取任务，需先终止才能开启下一次任务", {
            "__type__": "update",
            "visible": False,
        }, {"__type__": "update", "visible": True}


def close1c():
    global ps1c
    if ps1c != []:
        for p1c in ps1c:
            try:
                kill_process(p1c.pid)
            except:
                traceback.print_exc()
        ps1c = []
    return (
        "已终止所有语义token进程",
        {"__type__": "update", "visible": True},
        {"__type__": "update", "visible": False},
    )


#####inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,cnhubert_base_dir,pretrained_s2G
ps1abc = []


def open1abc(
    inp_text,
    inp_wav_dir,
    exp_name,
    gpu_numbers1a,
    gpu_numbers1Ba,
    gpu_numbers1c,
    bert_pretrained_dir,
    ssl_pretrained_dir,
    pretrained_s2G_path,
):
    global ps1abc
    if ps1abc == []:
        opt_dir = "%s/%s" % (exp_root, exp_name)
        try:
            #############################1a
            path_text = "%s/2-name2text.txt" % opt_dir
            if os.path.exists(path_text) == False:
                config = {
                    "inp_text": inp_text,
                    "inp_wav_dir": inp_wav_dir,
                    "exp_name": exp_name,
                    "opt_dir": opt_dir,
                    "bert_pretrained_dir": bert_pretrained_dir,
                    "is_half": str(is_half),
                }
                gpu_names = gpu_numbers1a.split("-")
                all_parts = len(gpu_names)
                for i_part in range(all_parts):
                    config.update(
                        {
                            "i_part": str(i_part),
                            "all_parts": str(all_parts),
                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                        }
                    )
                    os.environ.update(config)
                    cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py' % python_exec
                    print(cmd)
                    p = Popen(cmd, shell=True)
                    ps1abc.append(p)
                yield "进度：1a-ing", {"__type__": "update", "visible": False}, {
                    "__type__": "update",
                    "visible": True,
                }
                for p in ps1abc:
                    p.wait()

                opt = []
                for i_part in range(
                    all_parts
                ):  # txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
                    txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
                    with open(txt_path, "r", encoding="utf8") as f:
                        opt += f.read().strip("\n").split("\n")
                    os.remove(txt_path)
                with open(path_text, "w", encoding="utf8") as f:
                    f.write("\n".join(opt) + "\n")

            yield "进度：1a-done", {"__type__": "update", "visible": False}, {
                "__type__": "update",
                "visible": True,
            }
            ps1abc = []
            #############################1b
            config = {
                "inp_text": inp_text,
                "inp_wav_dir": inp_wav_dir,
                "exp_name": exp_name,
                "opt_dir": opt_dir,
                "cnhubert_base_dir": ssl_pretrained_dir,
            }
            gpu_names = gpu_numbers1Ba.split("-")
            all_parts = len(gpu_names)
            for i_part in range(all_parts):
                config.update(
                    {
                        "i_part": str(i_part),
                        "all_parts": str(all_parts),
                        "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                    }
                )
                os.environ.update(config)
                cmd = (
                    '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'
                    % python_exec
                )
                print(cmd)
                p = Popen(cmd, shell=True)
                ps1abc.append(p)
            yield "进度：1a-done, 1b-ing", {"__type__": "update", "visible": False}, {
                "__type__": "update",
                "visible": True,
            }
            for p in ps1abc:
                p.wait()
            yield "进度：1a1b-done", {"__type__": "update", "visible": False}, {
                "__type__": "update",
                "visible": True,
            }
            ps1abc = []
            #############################1c
            path_semantic = "%s/6-name2semantic.tsv" % opt_dir
            if os.path.exists(path_semantic) == False:
                config = {
                    "inp_text": inp_text,
                    "exp_name": exp_name,
                    "opt_dir": opt_dir,
                    "pretrained_s2G": pretrained_s2G_path,
                    "s2config_path": "GPT_SoVITS/configs/s2.json",
                }
                gpu_names = gpu_numbers1c.split("-")
                all_parts = len(gpu_names)
                for i_part in range(all_parts):
                    config.update(
                        {
                            "i_part": str(i_part),
                            "all_parts": str(all_parts),
                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                        }
                    )
                    os.environ.update(config)
                    cmd = (
                        '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'
                        % python_exec
                    )
                    print(cmd)
                    p = Popen(cmd, shell=True)
                    ps1abc.append(p)
                yield "进度：1a1b-done, 1cing", {"__type__": "update", "visible": False}, {
                    "__type__": "update",
                    "visible": True,
                }
                for p in ps1abc:
                    p.wait()

                opt = ["item_name	semantic_audio"]
                for i_part in range(all_parts):
                    semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
                    with open(semantic_path, "r", encoding="utf8") as f:
                        opt += f.read().strip("\n").split("\n")
                    os.remove(semantic_path)
                with open(path_semantic, "w", encoding="utf8") as f:
                    f.write("\n".join(opt) + "\n")
                yield "进度：all-done", {"__type__": "update", "visible": False}, {
                    "__type__": "update",
                    "visible": True,
                }
            ps1abc = []
            yield "一键三连进程结束", {"__type__": "update", "visible": True}, {
                "__type__": "update",
                "visible": False,
            }
        except:
            traceback.print_exc()
            close1abc()
            yield "一键三连中途报错", {"__type__": "update", "visible": True}, {
                "__type__": "update",
                "visible": False,
            }
    else:
        yield "已有正在进行的一键三连任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {
            "__type__": "update",
            "visible": True,
        }


def close1abc():
    global ps1abc
    if ps1abc != []:
        for p1abc in ps1abc:
            try:
                kill_process(p1abc.pid)
            except:
                traceback.print_exc()
        ps1abc = []
    return (
        "已终止所有一键三连进程",
        {"__type__": "update", "visible": True},
        {"__type__": "update", "visible": False},
    )


with gr.Blocks(title="GPT-SoVITS WebUI") as app:
    gr.Markdown(
        value="本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>."
    )
    with gr.Tabs():
        with gr.TabItem("0-前置数据集获取工具"):  # 提前随机切片防止uvr5爆内存->uvr5->slicer->asr->打标
            gr.Markdown(value="0a-UVR5人声伴奏分离&去混响去延迟工具")
            with gr.Row():
                if_uvr5 = gr.Checkbox(label="是否开启UVR5-WebUI", show_label=True)
                uvr5_info = gr.Textbox(label="UVR5进程输出信息")
            gr.Markdown(value="0b-语音切分工具")
            with gr.Row():
                with gr.Row():
                    slice_inp_path = gr.Textbox(label="音频自动切分输入路径，可文件可文件夹", value="")
                    slice_opt_root = gr.Textbox(
                        label="切分后的子音频的输出根目录", value="output/slicer_opt"
                    )
                    threshold = gr.Textbox(
                        label="threshold:音量小于这个值视作静音的备选切割点", value="-34"
                    )
                    min_length = gr.Textbox(
                        label="min_length:每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值", value="4000"
                    )
                    min_interval = gr.Textbox(label="min_interval:最短切割间隔", value="300")
                    hop_size = gr.Textbox(
                        label="hop_size:怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）", value="10"
                    )
                    max_sil_kept = gr.Textbox(
                        label="max_sil_kept:切完后静音最多留多长", value="500"
                    )
                with gr.Row():
                    open_slicer_button = gr.Button(
                        "开启语音切割", variant="primary", visible=True
                    )
                    close_slicer_button = gr.Button(
                        "终止语音切割", variant="primary", visible=False
                    )
                    _max = gr.Slider(
                        minimum=0,
                        maximum=1,
                        step=0.05,
                        label="max:归一化后最大值多少",
                        value=0.9,
                        interactive=True,
                    )
                    alpha = gr.Slider(
                        minimum=0,
                        maximum=1,
                        step=0.05,
                        label="alpha_mix:混多少比例归一化后音频进来",
                        value=0.25,
                        interactive=True,
                    )
                    n_process = gr.Slider(
                        minimum=1,
                        maximum=n_cpu,
                        step=1,
                        label="切割使用的进程数",
                        value=4,
                        interactive=True,
                    )
                    slicer_info = gr.Textbox(label="语音切割进程输出信息")
            gr.Markdown(value="0c-中文批量离线ASR工具")
            with gr.Row():
                open_asr_button = gr.Button(
                    "开启离线批量ASR", variant="primary", visible=True
                )
                close_asr_button = gr.Button(
                    "终止ASR进程", variant="primary", visible=False
                )
                asr_inp_dir = gr.Textbox(
                    label="批量ASR(中文only)输入文件夹路径",
                    value="D:\\RVC1006\\GPT-SoVITS\\raw\\xxx",
                    interactive=True,
                )
                asr_info = gr.Textbox(label="ASR进程输出信息")
            gr.Markdown(value="0d-语音文本校对标注工具")
            with gr.Row():
                if_label = gr.Checkbox(label="是否开启打标WebUI", show_label=True)
                path_list = gr.Textbox(
                    label="打标数据标注文件路径",
                    value="D:\\RVC1006\\GPT-SoVITS\\raw\\xxx.list",
                    interactive=True,
                )
                label_info = gr.Textbox(label="打标工具进程输出信息")
            if_label.change(change_label, [if_label, path_list], [label_info])
            if_uvr5.change(change_uvr5, [if_uvr5], [uvr5_info])
            open_asr_button.click(
                open_asr, [asr_inp_dir], [asr_info, open_asr_button, close_asr_button]
            )
            close_asr_button.click(
                close_asr, [], [asr_info, open_asr_button, close_asr_button]
            )
            open_slicer_button.click(
                open_slice,
                [
                    slice_inp_path,
                    slice_opt_root,
                    threshold,
                    min_length,
                    min_interval,
                    hop_size,
                    max_sil_kept,
                    _max,
                    alpha,
                    n_process,
                ],
                [slicer_info, open_slicer_button, close_slicer_button],
            )
            close_slicer_button.click(
                close_slice, [], [slicer_info, open_slicer_button, close_slicer_button]
            )
        with gr.TabItem("1-GPT-SoVITS-TTS"):
            with gr.Row():
                exp_name = gr.Textbox(label="*实验/模型名", value="xxx", interactive=True)
                gpu_info = gr.Textbox(
                    label="显卡信息", value=gpu_info, visible=True, interactive=False
                )
                pretrained_s2G = gr.Textbox(
                    label="预训练的SoVITS-G模型路径",
                    value="GPT_SoVITS/pretrained_models/s2G488k.pth",
                    interactive=True,
                )
                pretrained_s2D = gr.Textbox(
                    label="预训练的SoVITS-D模型路径",
                    value="GPT_SoVITS/pretrained_models/s2D488k.pth",
                    interactive=True,
                )
                pretrained_s1 = gr.Textbox(
                    label="预训练的GPT模型路径",
                    value="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
                    interactive=True,
                )
            with gr.TabItem("1A-训练集格式化工具"):
                gr.Markdown(value="输出logs/实验名目录下应有23456开头的文件和文件夹")
                with gr.Row():
                    inp_text = gr.Textbox(
                        label="*文本标注文件",
                        value=r"D:\RVC1006\GPT-SoVITS\raw\xxx.list",
                        interactive=True,
                    )
                    inp_wav_dir = gr.Textbox(
                        label="*训练集音频文件目录",
                        value=r"D:\RVC1006\GPT-SoVITS\raw\xxx",
                        interactive=True,
                    )
                gr.Markdown(value="1Aa-文本内容")
                with gr.Row():
                    gpu_numbers1a = gr.Textbox(
                        label="GPU卡号以-分割，每个卡号一个进程",
                        value="%s-%s" % (gpus, gpus),
                        interactive=True,
                    )
                    bert_pretrained_dir = gr.Textbox(
                        label="预训练的中文BERT模型路径",
                        value="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
                        interactive=False,
                    )
                    button1a_open = gr.Button("开启文本获取", variant="primary", visible=True)
                    button1a_close = gr.Button(
                        "终止文本获取进程", variant="primary", visible=False
                    )
                    info1a = gr.Textbox(label="文本进程输出信息")
                gr.Markdown(value="1Ab-SSL自监督特征提取")
                with gr.Row():
                    gpu_numbers1Ba = gr.Textbox(
                        label="GPU卡号以-分割，每个卡号一个进程",
                        value="%s-%s" % (gpus, gpus),
                        interactive=True,
                    )
                    cnhubert_base_dir = gr.Textbox(
                        label="预训练的SSL模型路径",
                        value="GPT_SoVITS/pretrained_models/chinese-hubert-base",
                        interactive=False,
                    )
                    button1b_open = gr.Button(
                        "开启SSL提取", variant="primary", visible=True
                    )
                    button1b_close = gr.Button(
                        "终止SSL提取进程", variant="primary", visible=False
                    )
                    info1b = gr.Textbox(label="SSL进程输出信息")
                gr.Markdown(value="1Ac-语义token提取")
                with gr.Row():
                    gpu_numbers1c = gr.Textbox(
                        label="GPU卡号以-分割，每个卡号一个进程",
                        value="%s-%s" % (gpus, gpus),
                        interactive=True,
                    )
                    button1c_open = gr.Button(
                        "开启语义token提取", variant="primary", visible=True
                    )
                    button1c_close = gr.Button(
                        "终止语义token提取进程", variant="primary", visible=False
                    )
                    info1c = gr.Textbox(label="语义token提取进程输出信息")
                gr.Markdown(value="1Aabc-训练集格式化一键三连")
                with gr.Row():
                    button1abc_open = gr.Button(
                        "开启一键三连", variant="primary", visible=True
                    )
                    button1abc_close = gr.Button(
                        "终止一键三连", variant="primary", visible=False
                    )
                    info1abc = gr.Textbox(label="一键三连进程输出信息")
            button1a_open.click(
                open1a,
                [inp_text, inp_wav_dir, exp_name, gpu_numbers1a, bert_pretrained_dir],
                [info1a, button1a_open, button1a_close],
            )
            button1a_close.click(close1a, [], [info1a, button1a_open, button1a_close])
            button1b_open.click(
                open1b,
                [inp_text, inp_wav_dir, exp_name, gpu_numbers1Ba, cnhubert_base_dir],
                [info1b, button1b_open, button1b_close],
            )
            button1b_close.click(close1b, [], [info1b, button1b_open, button1b_close])
            button1c_open.click(
                open1c,
                [inp_text, exp_name, gpu_numbers1c, pretrained_s2G],
                [info1c, button1c_open, button1c_close],
            )
            button1c_close.click(close1c, [], [info1c, button1c_open, button1c_close])
            button1abc_open.click(
                open1abc,
                [
                    inp_text,
                    inp_wav_dir,
                    exp_name,
                    gpu_numbers1a,
                    gpu_numbers1Ba,
                    gpu_numbers1c,
                    bert_pretrained_dir,
                    cnhubert_base_dir,
                    pretrained_s2G,
                ],
                [info1abc, button1abc_open, button1abc_close],
            )
            button1abc_close.click(
                close1abc, [], [info1abc, button1abc_open, button1abc_close]
            )
            with gr.TabItem("1B-微调训练"):
                gr.Markdown(value="1Ba-SoVITS训练。用于分享的模型文件输出在SoVITS_weights下。")
                with gr.Row():
                    batch_size = gr.Slider(
                        minimum=1,
                        maximum=40,
                        step=1,
                        label=i18n("每张显卡的batch_size"),
                        value=default_batch_size,
                        interactive=True,
                    )
                    total_epoch = gr.Slider(
                        minimum=1,
                        maximum=20,
                        step=1,
                        label=i18n("总训练轮数total_epoch，不建议太高"),
                        value=8,
                        interactive=True,
                    )
                    text_low_lr_rate = gr.Slider(
                        minimum=0.2,
                        maximum=0.6,
                        step=0.05,
                        label="文本模块学习率权重",
                        value=0.4,
                        interactive=True,
                    )
                    save_every_epoch = gr.Slider(
                        minimum=1,
                        maximum=50,
                        step=1,
                        label=i18n("保存频率save_every_epoch"),
                        value=4,
                        interactive=True,
                    )
                    if_save_latest = gr.Checkbox(
                        label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),
                        value=True,
                        interactive=True,
                        show_label=True,
                    )
                    if_save_every_weights = gr.Checkbox(
                        label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
                        value=True,
                        interactive=True,
                        show_label=True,
                    )
                    gpu_numbers1Ba = gr.Textbox(
                        label="GPU卡号以-分割，每个卡号一个进程",
                        value="%s" % (gpus),
                        interactive=True,
                    )
                with gr.Row():
                    button1Ba_open = gr.Button(
                        "开启SoVITS训练", variant="primary", visible=True
                    )
                    button1Ba_close = gr.Button(
                        "终止SoVITS训练", variant="primary", visible=False
                    )
                    info1Ba = gr.Textbox(label="SoVITS训练进程输出信息")
                gr.Markdown(value="1Bb-GPT训练。用于分享的模型文件输出在GPT_weights下。")
                with gr.Row():
                    batch_size1Bb = gr.Slider(
                        minimum=1,
                        maximum=40,
                        step=1,
                        label=i18n("每张显卡的batch_size"),
                        value=default_batch_size,
                        interactive=True,
                    )
                    total_epoch1Bb = gr.Slider(
                        minimum=2,
                        maximum=100,
                        step=1,
                        label=i18n("总训练轮数total_epoch"),
                        value=15,
                        interactive=True,
                    )
                    if_save_latest1Bb = gr.Checkbox(
                        label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),
                        value=True,
                        interactive=True,
                        show_label=True,
                    )
                    if_save_every_weights1Bb = gr.Checkbox(
                        label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
                        value=True,
                        interactive=True,
                        show_label=True,
                    )
                    save_every_epoch1Bb = gr.Slider(
                        minimum=1,
                        maximum=50,
                        step=1,
                        label=i18n("保存频率save_every_epoch"),
                        value=5,
                        interactive=True,
                    )
                    gpu_numbers1Bb = gr.Textbox(
                        label="GPU卡号以-分割，每个卡号一个进程",
                        value="%s" % (gpus),
                        interactive=True,
                    )
                with gr.Row():
                    button1Bb_open = gr.Button(
                        "开启GPT训练", variant="primary", visible=True
                    )
                    button1Bb_close = gr.Button(
                        "终止GPT训练", variant="primary", visible=False
                    )
                    info1Bb = gr.Textbox(label="GPT训练进程输出信息")
            button1Ba_open.click(
                open1Ba,
                [
                    batch_size,
                    total_epoch,
                    exp_name,
                    text_low_lr_rate,
                    if_save_latest,
                    if_save_every_weights,
                    save_every_epoch,
                    gpu_numbers1Ba,
                    pretrained_s2G,
                    pretrained_s2D,
                ],
                [info1Ba, button1Ba_open, button1Ba_close],
            )
            button1Ba_close.click(
                close1Ba, [], [info1Ba, button1Ba_open, button1Ba_close]
            )
            button1Bb_open.click(
                open1Bb,
                [
                    batch_size1Bb,
                    total_epoch1Bb,
                    exp_name,
                    if_save_latest1Bb,
                    if_save_every_weights1Bb,
                    save_every_epoch1Bb,
                    gpu_numbers1Bb,
                    pretrained_s1,
                ],
                [info1Bb, button1Bb_open, button1Bb_close],
            )
            button1Bb_close.click(
                close1Bb, [], [info1Bb, button1Bb_open, button1Bb_close]
            )
            with gr.TabItem("1C-推理"):
                gr.Markdown(
                    value="选择训练完存放在SoVITS_weights和GPT_weights下的模型。默认的一个是底模，体验5秒Zero Shot TTS用。"
                )
                with gr.Row():
                    GPT_dropdown = gr.Dropdown(
                        label="*GPT模型列表",
                        choices=sorted(GPT_names),
                        value=pretrained_gpt_name,
                    )
                    SoVITS_dropdown = gr.Dropdown(
                        label="*SoVITS模型列表",
                        choices=sorted(SoVITS_names),
                        value=pretrained_sovits_name,
                    )
                    gpu_number_1C = gr.Textbox(
                        label="GPU卡号,只能填1个整数", value=gpus, interactive=True
                    )
                    refresh_button = gr.Button("刷新模型路径", variant="primary")
                    refresh_button.click(
                        fn=change_choices,
                        inputs=[],
                        outputs=[SoVITS_dropdown, GPT_dropdown],
                    )
                with gr.Row():
                    if_tts = gr.Checkbox(label="是否开启TTS推理WebUI", show_label=True)
                    tts_info = gr.Textbox(label="TTS推理WebUI进程输出信息")
                    if_tts.change(
                        change_tts_inference,
                        [
                            if_tts,
                            bert_pretrained_dir,
                            cnhubert_base_dir,
                            gpu_number_1C,
                            GPT_dropdown,
                            SoVITS_dropdown,
                        ],
                        [tts_info],
                    )
        with gr.TabItem("2-GPT-SoVITS-变声"):
            gr.Markdown(value="施工中，请静候佳音")

    """
            os.environ["gpt_path"]=gpt_path
            os.environ["sovits_path"]=sovits_path#bert_pretrained_dir
            os.environ["cnhubert_base_path"]=cnhubert_base_path#cnhubert_base_dir
            os.environ["bert_path"]=bert_path
            os.environ["_CUDA_VISIBLE_DEVICES"]=gpu_number
    """

    app.queue(concurrency_count=511, max_size=1022).launch(
        server_name="0.0.0.0",
        inbrowser=True,
        server_port=webui_port_main,
        quiet=True,
    )
